Feature Factorization for top-n Recommendation: From item rating to features relevance

In the last decade, collaborative filtering approaches have shown their effectiveness in computing accurate recommendations starting from the user-item matrix. Unfortunately, due to their inner nature, collaborative algorithms work very well with dense matrices but show their limits when they deal with sparse ones. In these cases, encoding user preferences only by means of past ratings may lead to unsatisfactory recommendations. In this paper we propose to exploit past user ratings to evaluate the relevance of every single feature within each profile thus moving from a user-item to a userfeature matrix. We then use matrix factorization techniques to compute recommendations. The evaluation has been performed on two datasets referring to different domains (music and books) and experimental results show that the proposed method outperforms the matrix factorization approach performed in the user-item space in terms of accuracy of results.